Inside the penal voluntary sector: Divided discourses of “helping” criminalized women
Bibliographic record
Abstract
Neoliberal austerity measures and welfare state retrenchment have meant that voluntary organizations around the globe are increasingly called upon to perform statutory social services. Despite a large and rising presence in criminal justice service delivery, volunteers and voluntary organizations have scarcely received scholarly analysis. This paper uses interviews, ethnography, and document analysis to explore the penal voluntary sector in Canada. Specifically, how individuals in the penal voluntary sector understand their roles in helping criminalized women and how these perspectives vary across different positions. This paper illuminates how agents occupying different helper positions cultivate divergent understandings of (and justifications for) the help they provide. Bourdieu’s field theory is mobilized to demonstrate how variegated discourses of helping co-exist, conflict, and impact the relational dynamics of the penal voluntary sector and its engagement with criminalized women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.047 | 0.066 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".